Lifeboat Foundation

There are contexts where human cognitive and emotional intelligence takes precedence over AI, which serves a supporting role in decision-making without overriding human judgment. Here, AI “protects” human cognitive processes from things like bias, heuristic thinking, or decision-making that activates the brain’s reward system and leads to incoherent or skewed results. In the human-first mode, artificial integrity can assist judicial processes by analyzing previous law cases and outcomes, for instance, without substituting a judge’s moral and ethical reasoning. For this to work well, the AI system would also have to show how it arrives at different conclusions and recommendations, considering any cultural context or values that apply differently across different regions or legal systems.

4 – Fusion Mode:

Artificial integrity in this mode is a synergy between human intelligence and AI capabilities combining the best of both worlds. Autonomous vehicles operating in Fusion Mode would have AI managing the vehicle’s operations, such as speed, navigation, and obstacle avoidance, while human oversight, potentially through emerging technologies like Brain-Computer Interfaces (BCIs), would offer real-time input on complex ethical dilemmas. For instance, in unavoidable crash situations, a BCI could enable direct communication between the human brain and AI, allowing ethical decision-making to occur in real-time, and blending AI’s precision with human moral reasoning. These kinds of advanced integrations between humans and machines will require artificial integrity at the highest level of maturity: artificial integrity would ensure not only technical excellence but ethical robustness, to guard against any exploitation or manipulation of neural data as it prioritizes human safety and autonomy.

What processes are responsible for dust storms on Mars? This is what a study presented today at the American Geophysical Union 2024 Fall Meeting hopes to address as a pair of researchers from the University of Colorado Boulder (CU Boulder) investigated the causes behind the massive dust storms on Mars, which periodically grow large enough to engulf the entire planet. This study holds the potential to help researchers predict dust storms on Mars, which could help current and future robotic missions survive these calamities, along with future human crews to the Red Planet.

“Dust storms have a significant effect on rovers and landers on Mars, not to mention what will happen during future crewed missions to Mars,” said Heshani Pieris, who is a PhD Candidate in planetary science at CU Boulder and lead author of the study. “This dust is very light and sticks to everything.”

For the study, the researchers examined 15 (Earth) years of data obtained from NASA’s Mars Reconnaissance Orbiter (MRO) to ascertain the processes responsible for kickstarting dust storms. After analyzing countless datasets of Martian surface temperatures, the researchers found that 68 percent of large dust storms on Mars resulted from spikes in surface temperatures during periods of increased sunlight through Mars’ thin atmosphere.

A year later, he got a myoelectric arm, a type of prosthetic powered by the electrical signals in his residual limb’s muscles. But Smith hardly used it because it was “very, very slow” and had a limited range of movements. He could open and close the hand, but not do much else. He tried other robotic arms over the years, but they had similar problems.

“They’re just not super functional,” he says. “There’s a massive delay between executing a function and then having the prosthetic actually do it. In my day-to-day life, it just became faster to figure out other ways to do things.”

Recently, he’s been trying out a new system by Austin-based startup Phantom Neuro that has the potential to provide more lifelike control of prosthetic limbs. The company is building a thin, flexible muscle implant to allow amputees a wider, more natural range of movement just by thinking about the gestures they want to make.

MovieNet mimics the brain to analyze dynamic scenes with exceptional accuracy.

By simulating how the human brain processes a moving world, Scripps researchers have reached a breakthrough in AI as current models only recognize still images.

Training AI models today isn’t just about designing better architectures—it’s also about managing data efficiently. Modern models require vast datasets and need those datasets delivered quickly to GPUs and other accelerators. The problem? Traditional data loading systems often lag behind, slowing everything down. These older systems rely heavily on process-based methods that struggle to keep up with the demand, leading to GPU downtime, longer training sessions, and higher costs. This becomes even more frustrating when you’re trying to scale up or work with a mix of data types.

To tackle these issues, Meta AI has developed SPDL (Scalable and Performant Data Loading), a tool designed to improve how data is delivered during AI training. SPDL uses thread-based loading, which is a departure from the traditional process-based approach, to speed things up. It handles data from all sorts of sources—whether you’re pulling from the cloud or a local storage system—and integrates it seamlessly into your training workflow.

SPDL was built with scalability in mind. It works across distributed systems, so whether you’re training on a single GPU or a large cluster, SPDL has you covered. It’s also designed to work well with PyTorch, one of the most widely used AI frameworks, making it easier for teams to adopt. And since it’s open-source, anyone can take advantage of it or even contribute to its improvement.

The hallmark of a good AI model is its ability to work the same in different groups, settings, and situations. See how these NCI researchers used in-house and external images to test their prostate model’s generalizability.

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Scientific literature is growing rapidly, meaning scientists are increasingly unable to keep up with all of the latest developments in research. AI large language models, though, can read and “digest” information much more quickly than their human counterparts, making them the perfect tools to conduct massive literature reviews. Recent research shows they’re also very accurate at predicting the results of studies that they’ve never read before. Let’s take a look.

Continue reading “AIs Predict Research Results Without Doing Research” »

However, while Google’s achievements have been noted for advancing the field, experts say that quantum computing still has no real-world uses — yet.

“We need a ChatGPT moment for quantum,” Francesco Ricciuti, associate at venture capital firm Runa Capital, told CNBC on Tuesday, referencing OpenAI’s chatbot that has been credited with driving the boom in artificial intelligence. “This is probably not that.”

Proponents of quantum computing claim it will be able to solve problems that current computers can’t.

AI can predict human intelligence by looking at the connections of a working human brain. Neuroscientists can predict intelligence from brain structure and function—to a point. Previous studies have suggested that intelligence is widely distributed across the brain.

Kirsten Hilger and colleagues used machine learning models to predict multiple kinds of intelligence from brain connections of 806 healthy adults while resting and while completing tasks. The article is published in PNAS Nexus.

Fluid intelligence includes inductive and deductive reasoning abilities that do not rely on context, while crystallized intelligence reflects the ability to apply knowledge from individual experience and culture. General intelligence captures both types of intelligence.